Computer-aided approaches utilizing deep learning models have become prominent in the domain of medical image processing [1]}. The amount and diversity of training data used to develop these models are important for model success and generalizability [2]}, [3]}, [4]}. Currently, the inadequacy of medical data sources and labeled data have become a bottleneck and led to poor performance of the deep learning based solutions [5]}. In order to overcome these issues, there are several initiatives to form diverse datasets to train reliable and robust models that have good generalization ability and clinical usability. EndoCV Challenges incorporates diverse endoscopy video frames from several institutions worldwide, including different modalities and organs to utilize deep learning methods to detect artifacts and diseases [6]}, [7]}, [8]}, [9]}. BraTS Challenges brings multi-institutional multi-parametric magnetic resonance imaging (mpMRI) scans for the analysis of brain tumors and the dataset has been continuously growing [10]}. Although these initiatives are very important for reliable and clinical-ready models, they are not feasible to scale because it requires a tremendous work. First of all, it is difficult to represent whole distribution (e.g., minority and under-represented groups) as it requires healthy collaborations with many institutions and immense annotations. Secondly, data properties such as image modalities and resolutions are in a constant change that leads to distribution shift over time; therefore, collecting and processing all the data for once does not work either. Moreover, due to the data privacy regulations, collecting sensitive patient data from different institutions and hospitals is not always applicable. The federated learning (FL) concept offers a solution in such situations where data privacy and ownership are a problem by enabling collaborators train a common global model without disclosing their local data [11]}, [12]}. Several studies employing FL approaches in the medical domain have reported successful results [13]}, [2]}, [3]}. These studies have drawn the attention of researchers into FL for medical imaging and made it a popular research field recently.
